code: def k_fold_cross_valid(
      k,
      epochs,
      verbose_epoch,
      X_train,
      y_train,
      learning_rate,
      weight_decay,
      batch_size,
  ):
      """Conducts k-fold cross validation for the model."""
      assert k > 1
      fold_size = X_train.shape[0] // k
  
      train_loss_sum = 0.0
      test_loss_sum = 0.0
      for test_idx in range(k):
          X_val_test = X_train[
              test_idx * fold_size : (test_idx + 1) * fold_size, :
          ]
          y_val_test = y_train[
              test_idx * fold_size : (test_idx + 1) * fold_size
          ]
          val_train_defined = False
          for i in range(k):
              if i != test_idx:
                  X_cur_fold = X_train[
                      i * fold_size : (i + 1) * fold_size, :
                  ]
                  y_cur_fold = y_train[
                      i * fold_size : (i + 1) * fold_size
                  ]
                  if not val_train_defined:
                      X_val_train = X_cur_fold
                      y_val_train = y_cur_fold
                      val_train_defined = True
                  else:
                      X_val_train = np.concatenate(
                          [X_val_train, X_cur_fold], axis=0
                      )
                      y_val_train = np.concatenate(
                          [y_val_train, y_cur_fold], axis=0
                      )
          net = get_net()
          train_loss = train(
              net,
              X_val_train,
              y_val_train,
              epochs,
              verbose_epoch,
              learning_rate,
              weight_decay,
              batch_size,
          )
          train_loss_sum += train_loss
          test_loss = get_rmse_log(net, X_val_test, y_val_test)
          print(f"Test loss: {test_loss}")
          test_loss_sum += test_loss
      return train_loss_sum / k, test_loss_sum / k
  
file_path: mxnet/example/gluon/house_prices/kaggle_k_fold_cross_validation.py
imports: import numpy
  import pandas
  from mxnet import autograd
  from mxnet import gluon
  from mxnet import np
label_desc: Use image node_id 4 for calling a global function with example usage: k_fold_cross_valid(k, epochs, verbose_epoch, X_train, y_train, learning_rate, weight_decay, batch_size) and returns: unknown,  unknown